{"total":99,"rows":[{"id":"2202.07282","title":"Adaptive Conformal Predictions for Time Series","created":"2022-02-15","cited_by_count":161,"area":null},{"id":"2410.04803","title":"Timer-XL: Long-Context Transformers for Unified Time Series Forecasting","created":"2024-10-07","cited_by_count":87,"area":null},{"id":"2104.05522","title":"Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx","created":"2021-04-12","cited_by_count":64,"area":null},{"id":"2509.26468","title":"fev-bench: A Realistic Benchmark for Time Series Forecasting","created":"2025-09-30","cited_by_count":64,"area":null},{"id":"2602.12147","title":"It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks","created":"2026-02-12","cited_by_count":33,"area":null},{"id":"2207.02832","title":"Distributional neural networks for electricity price forecasting","created":"2022-07-06","cited_by_count":24,"area":null},{"id":"2504.04011","title":"Foundation Models for Time Series: A Survey","created":"2025-04-05","cited_by_count":23,"area":null},{"id":"2505.23116","title":"CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables","created":"2025-05-29","cited_by_count":22,"area":null},{"id":"2205.13826","title":"Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows","created":"2022-05-27","cited_by_count":14,"area":null},{"id":"2603.08032","title":"GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables","created":"2026-03-09","cited_by_count":13,"area":null},{"id":"2505.18442","title":"Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting","created":"2025-05-24","cited_by_count":12,"area":null},{"id":"2506.03128","title":"Zero-Shot Time Series Forecasting with Covariates via In-Context Learning","created":"2025-06-03","cited_by_count":12,"area":null},{"id":"2112.02663","title":"ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting","created":"2021-12-05","cited_by_count":11,"area":null},{"id":"2205.11439","title":"Probabilistic forecasting of German electricity imbalance prices","created":"2022-05-23","cited_by_count":10,"area":null},{"id":"2401.17441","title":"Explaining Predictive Uncertainty by Exposing Second-Order Effects","created":"2024-01-30","cited_by_count":10,"area":null},{"id":"2602.13802","title":"Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting","created":"2026-02-14","cited_by_count":10,"area":null},{"id":"2311.07289","title":"A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market","created":"2023-11-13","cited_by_count":7,"area":null},{"id":"2404.02270","title":"Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression","created":"2024-04-02","cited_by_count":7,"area":null},{"id":"2506.22039","title":"UniCA: Unified Covariate Adaptation for Time Series Foundation Model","created":"2025-06-27","cited_by_count":7,"area":null},{"id":"2203.00937","title":"ES-dRNN with Dynamic Attention for Short-Term Load Forecasting","created":"2022-03-02","cited_by_count":6,"area":null},{"id":"2212.04805","title":"Understanding electricity prices beyond the merit order principle using explainable AI","created":"2022-12-09","cited_by_count":6,"area":null},{"id":"2304.14774","title":"A feature selection method based on Shapley values robust to concept shift in regression","created":"2023-04-28","cited_by_count":6,"area":null},{"id":"2402.06714","title":"Electricity Price Forecasting in the Irish Balancing Market","created":"2024-02-09","cited_by_count":6,"area":null},{"id":"2510.12681","title":"CoRA: Covariate-Aware Adaptation of Time Series Foundation Models","created":"2025-10-14","cited_by_count":6,"area":null},{"id":"2404.02722","title":"On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices","created":"2024-04-03","cited_by_count":5,"area":null},{"id":"2410.03806","title":"Metadata Matters for Time Series: Informative Forecasting with Transformers","created":"2024-10-04","cited_by_count":5,"area":null},{"id":"2503.24007","title":"CITRAS: Covariate-Informed Transformer for Time Series Forecasting","created":"2025-03-31","cited_by_count":5,"area":null},{"id":"2310.03339","title":"Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs","created":"2023-10-05","cited_by_count":4,"area":null},{"id":"2412.06112","title":"PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems","created":"2024-12-09","cited_by_count":4,"area":null},{"id":"2308.15443","title":"Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2407.08750","title":"Online Distributional Regression","created":"2024-06-26","cited_by_count":3,"area":null},{"id":"2502.04935","title":"Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market","created":"2025-02-07","cited_by_count":3,"area":null},{"id":"2507.13250","title":"Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets","created":"2025-07-17","cited_by_count":3,"area":null},{"id":"2512.20761","title":"TS-Arena -- A Live Forecast Pre-Registration Platform","created":"2025-12-23","cited_by_count":3,"area":null},{"id":"2203.03342","title":"High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks","created":"2022-03-07","cited_by_count":2,"area":null},{"id":"2311.14033","title":"Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows","created":"2023-11-23","cited_by_count":2,"area":null},{"id":"2411.03372","title":"Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2411.13921","title":"NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape","created":"2024-11-21","cited_by_count":2,"area":null},{"id":"2502.06830","title":"OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting","created":"2025-02-05","cited_by_count":2,"area":null},{"id":"2504.02518","title":"Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2506.00044","title":"Probabilistic intraday electricity price forecasting using generative machine learning","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2506.19894","title":"Explaining deep neural network models for electricity price forecasting with XAI","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2509.23159","title":"ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting","created":"2025-09-27","cited_by_count":2,"area":null},{"id":"2601.09237","title":"XLinear: A Lightweight and Accurate MLP-Based Model for Long-Term Time Series Forecasting with Exogenous Inputs","created":"2026-01-14","cited_by_count":2,"area":null},{"id":"2603.15802","title":"Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables","created":"2026-03-16","cited_by_count":2,"area":null},{"id":"2107.04091","title":"Ensembles of Randomized NNs for Pattern-based Time Series Forecasting","created":"2021-07-08","cited_by_count":1,"area":null},{"id":"2404.17683","title":"Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach","created":"2024-04-26","cited_by_count":1,"area":null},{"id":"2405.11372","title":"ReModels: Quantile Regression Averaging models","created":"2024-05-18","cited_by_count":1,"area":null},{"id":"2405.14893","title":"Revisiting Day-ahead Electricity Price: Simple Model Save Millions","created":"2024-05-20","cited_by_count":1,"area":null},{"id":"2412.00123","title":"Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression","created":"2024-11-28","cited_by_count":1,"area":null},{"id":"2412.07787","title":"Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2506.11050","title":"NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2509.19417","title":"Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2601.02856","title":"Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2603.06726","title":"Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2604.24705","title":"Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting","created":"2026-04-27","cited_by_count":1,"area":null},{"id":"2605.17730","title":"L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2609.00089","title":"Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2106.05860","title":"DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting","created":"2021-06-07","cited_by_count":0,"area":null},{"id":"2203.09170","title":"Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study","created":"2022-03-17","cited_by_count":0,"area":null},{"id":"2204.11735","title":"Forecasting Electricity Prices","created":"2022-04-25","cited_by_count":0,"area":null},{"id":"2209.01378","title":"RNN(p) for Power Consumption Forecasting","created":"2022-09-03","cited_by_count":0,"area":null},{"id":"2309.01336","title":"Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach","created":"2023-09-04","cited_by_count":0,"area":null},{"id":"2311.02610","title":"An adaptive standardisation methodology for Day-Ahead electricity price forecasting","created":"2023-11-05","cited_by_count":0,"area":null},{"id":"2403.16108","title":"A Transformer approach for Electricity Price Forecasting","created":"2024-03-24","cited_by_count":0,"area":null},{"id":"2405.07359","title":"Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation","created":"2024-05-12","cited_by_count":0,"area":null},{"id":"2406.14904","title":"Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference","created":"2024-06-21","cited_by_count":0,"area":null},{"id":"2408.04405","title":"Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces","created":"2024-08-08","cited_by_count":0,"area":null},{"id":"2408.05628","title":"Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods","created":"2024-08-10","cited_by_count":0,"area":null},{"id":"2503.11294","title":"Latent Space Representation of Electricity Market Curves: Maintaining Structural Integrity","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2506.10536","title":"Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2507.05284","title":"Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction","created":"2025-07-04","cited_by_count":0,"area":null},{"id":"2508.00040","title":"Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2509.14113","title":"From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2510.15011","title":"Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2511.04361","title":"Causal Regime Detection in Energy Markets With Augmented Time Series Structural Causal Models","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2511.05523","title":"The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2511.11701","title":"Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting","created":"2025-11-12","cited_by_count":0,"area":null},{"id":"2512.02070","title":"DPWMixer: Dual-Path Wavelet Mixer for Long-Term Time Series Forecasting","created":"2025-11-30","cited_by_count":0,"area":null},{"id":"2512.04690","title":"Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2602.01157","title":"Deep Time-Series Models Meet Volatility: Multi-Horizon Electricity Price Forecasting in the Australian National Electricity Market","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2602.10412","title":"LightGTS-Cov: Covariate-Enhanced Time Series Forecasting","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2603.03902","title":"PatchDecomp: Interpretable Patch-Based Time Series Forecasting","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.05092","title":"Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2604.26634","title":"Electricity price forecasting across Norway's five bidding zones in the post-crisis era","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2605.17045","title":"Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2605.22387","title":"Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.19118","title":"Analysing drivers and interdependencies in European electricity markets using XAI","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.06632","title":"Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2608.11359","title":"Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.17091","title":"Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.10589","title":"Halo: Improving forecast accuracy through heteroscedastic estimation","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.23223","title":"Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2609.24441","title":"MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.29108","title":"Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.34939","title":"XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36966","title":"JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.02256","title":"TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.17387","title":"Cross-Market Meta-Learning for Electricity Price Forecasting","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}